Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks
As an important raw material for the chemical industry, ethylene is one of the surest indicators that measure the development level of a country. The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. Du...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | International Journal of Chemical Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/4133703 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849691420183494656 |
|---|---|
| author | Xiangwu Deng Zhiping Peng Delong Cui |
| author_facet | Xiangwu Deng Zhiping Peng Delong Cui |
| author_sort | Xiangwu Deng |
| collection | DOAJ |
| description | As an important raw material for the chemical industry, ethylene is one of the surest indicators that measure the development level of a country. The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. Due to the limitations of online analytical instrumentation technology, diene yields are difficult to measure online. Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. First, the data feature vectors are fused and normalized. Then, the data are fed into a DBN consisting of two layers of restricted Boltzmann machines for unsupervised training, and finally, a DBN model is used to predict the diene yield. The experimental results show that the mean squared error of the test set with historical data is 1.15%, and the mean absolute percentage error of the measured data is 2.79%. The experimental results are provided to show the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-e7a20a6049eb48ae9a3bd9b430b35415 |
| institution | DOAJ |
| issn | 1687-8078 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Chemical Engineering |
| spelling | doaj-art-e7a20a6049eb48ae9a3bd9b430b354152025-08-20T03:21:02ZengWileyInternational Journal of Chemical Engineering1687-80782022-01-01202210.1155/2022/4133703Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief NetworksXiangwu Deng0Zhiping Peng1Delong Cui2College of Electronic Information EngineeringJiangmen PolytechnicCollege of Electronic Information EngineeringAs an important raw material for the chemical industry, ethylene is one of the surest indicators that measure the development level of a country. The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. Due to the limitations of online analytical instrumentation technology, diene yields are difficult to measure online. Motivated by this, this article has studied soft-sensing technology for measuring diene yields. A diene yield prediction method based on a deep belief network algorithm network is proposed, and the regularity of historical diene yield data is fully explored by the method. First, the data feature vectors are fused and normalized. Then, the data are fed into a DBN consisting of two layers of restricted Boltzmann machines for unsupervised training, and finally, a DBN model is used to predict the diene yield. The experimental results show that the mean squared error of the test set with historical data is 1.15%, and the mean absolute percentage error of the measured data is 2.79%. The experimental results are provided to show the effectiveness of the proposed method.http://dx.doi.org/10.1155/2022/4133703 |
| spellingShingle | Xiangwu Deng Zhiping Peng Delong Cui Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks International Journal of Chemical Engineering |
| title | Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks |
| title_full | Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks |
| title_fullStr | Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks |
| title_full_unstemmed | Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks |
| title_short | Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks |
| title_sort | research on soft sensing methods for measuring diene yields using deep belief networks |
| url | http://dx.doi.org/10.1155/2022/4133703 |
| work_keys_str_mv | AT xiangwudeng researchonsoftsensingmethodsformeasuringdieneyieldsusingdeepbeliefnetworks AT zhipingpeng researchonsoftsensingmethodsformeasuringdieneyieldsusingdeepbeliefnetworks AT delongcui researchonsoftsensingmethodsformeasuringdieneyieldsusingdeepbeliefnetworks |